Customized computational toxicology solutions that help identify hazards, understand mechanisms, and make confident safety decisions earlier in development.
Inotiv’s Computational Toxicology group (CompTox) combines cheminformatics, bioinformatics, exposure science, and predictive toxicology. We draw upon data from your assays, custom in-house resources, and relevant literature to deliver the insights you need throughout product development.
Due to the limited toxicological data available for many chemicals present in today’s environment, quantitative structure-activity relationship (QSAR) and read-across approaches have become increasingly important for prioritizing chemicals and guiding study selection. Our team has significant experience applying these methods to support hazard identification, chemical assessment, and regulatory decision-making including the evaluation of structural alerts associated with toxicological endpoints such as genotoxicity.
At Inotiv, we simplify the integration and interpretation of complex biological and toxicological datasets to support mechanistic understanding and safety assessments. Our approach uses both commercial software tools as well as novel computational tools and workflows developed and applied by our scientists. We tailor our approach to account for your program’s unique needs as well as the continual updating of source data, ensuring flexibility and relevance. Our expertise makes this complex work approachable and understandable, enabling our clients to leverage large data sources effectively and with confidence.
Inotiv's scientists help you maximize the information gained from in vitro data by placing experimental findings into a broader biological and exposure context. We leverage domain expertise to characterize mechanism of action and adverse outcome pathways (AOP/qAOPs). Using this information, in vitro to in vivo extrapolation (IVIVE) approaches can support dose selection, exposure assessment and estimation of toxicity reference or screening values. Our team has extensive experience in IVIVE analysis, including reverse toxicokinetic modeling and physiological-based pharmacokinetic (PBPK) modeling. We utilize PBPK modeling to help predict pharmacokinetics and pharmacodynamics from preclinical studies into estimates of exposure and internal dose. These models can also be used to evaluate population variability, life-stage differences, disease states, and the impact of drug-drug interactions or chemical mixtures on predicted exposures.
For a comprehensive list of our customizable services, please explore the expandable sections below.
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